Functionally Versatile and Highly Stable Chelator for <sup>111</sup>In and <sup>177</sup>Lu: Proof-of-Principle Prostate-Specific Membrane Antigen Targeting
Bibliographic record
Abstract
Abstract Here, we present the synthesis and characterization of a new potentially nonadentate chelator H4pypa and its bifunctional analogue tBu4pypa-C7-NHS conjugated to prostate-specific membrane antigen (PSMA)-targeting peptidomimetic (Glu-urea-Lys). H4pypa is very functionally versatile and biologically stable. Compared to the conventional chelators (e.g., DOTA, DTPA), H4pypa has outstanding affinities for both 111In (EC, t1/2 ? 2.8 days) and 177Lu (??,?, t1/2 ? 6.64 days). Its radiolabeled complexes were achieved at >98% radiochemical yield, RT within 10 min, at a ligand concentration as low as 10?6 M, with excellent stability in human serum over at least 5?7 days (<1% transchelation). The thermodynamic stabilities of the [M(pypa)]? complexes (M3+ = In3+, Lu3+, La3+) were dependent on the ionic radii, where the smaller In3+ has the highest pM value (30.5), followed by Lu3+ (22.6) and La3+ (19.9). All pM values are remarkably higher than those with DOTA, DTPA, H4octapa, H4octox, and H4neunpa. Moreover, the facile and versatile bifunctionalization enabled by the p-OH group in the central pyridyl bridge of the pypa scaffold (compound 14) allows incorporation of a variety of linkers for bioconjugation through easy nucleophilic substitution. In this work, an alkyl linker was selected to couple H4pypa to a PSMA-targeting pharmacophore, proving that the bioconjugation sacrifices neither the tumor-targeting nor the chelation properties. The biodistribution profiles of 111In- and 177Lu-labeled tracers are different, but promising, with the 177Lu analogue particularly outstanding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".